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; collaborate with the research teams at EPFL and Imperial College London Design, implement, and maintain core components of the verified LLM inference engine, including the runtime and glue code connecting
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code Developing agentic AI workflows for specification autoformalization, proof generation, and proof repair Build a strong network in the fields of formal verification, systems, and ML infrastructure
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) Ensuring medical grade software quality following industry best practices for patients' usage at home and for researchers in rehabilitation facilities Designing, coding, and driving the automation of test
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engages CSPG4, increases membrane tension, and activates the mechanosensitive ion channel PIEZO1, thereby promoting glioma proliferation. The successful candidate will build on this work to define
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. Participating in code reviews to ensure code quality and to provide & receive feedback. Applying IT security best practices, including e.g. Zero Trust and Least Privilege principles. Working with clients
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. Main duties and responsibilities Extract and harden a scoring engine from an existing Python/Streamlit prototype into robust, standalone code Rebuild the data layer from Neo4j to a Python-native graph
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Postal Code 1700 STATUS: EXPIRED X (formerly Twitter) Facebook LinkedIn Whatsapp More share options E-mail Pocket Viadeo Gmail Weibo Blogger Qzone YahooMail
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verifiable outcomes, such as mathematics, code, tool-use, and reasoning. Develop reward modeling, reward calibration and verifier-based training. Generate and validate synthetic or gym training tasks. Run
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this new hybrid network. Your code will directly enable the next generation of energy-efficient AI clusters. Project scope You will bridge the gap between custom optical hardware and standard AI software
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methodological expertise. Contact details for three professional references. Optional but encouraged: links to code repositories, analysis pipelines, experimental software, technical projects or other outputs